Overview

Dataset statistics

Number of variables37
Number of observations2126
Missing cells0
Missing cells (%)0.0%
Duplicate rows9
Duplicate rows (%)0.4%
Total size in memory614.7 KiB
Average record size in memory296.1 B

Variable types

Numeric22
Categorical15

Alerts

DR has constant value ""Constant
Dataset has 9 (0.4%) duplicate rowsDuplicates
b is highly overall correlated with eHigh correlation
e is highly overall correlated with bHigh correlation
LBE is highly overall correlated with LB and 3 other fieldsHigh correlation
LB is highly overall correlated with LBE and 3 other fieldsHigh correlation
AC is highly overall correlated with BHigh correlation
ASTV is highly overall correlated with MSTV and 1 other fieldsHigh correlation
MSTV is highly overall correlated with ASTV and 6 other fieldsHigh correlation
ALTV is highly overall correlated with MSTV and 4 other fieldsHigh correlation
DL is highly overall correlated with MSTV and 5 other fieldsHigh correlation
Width is highly overall correlated with MSTV and 6 other fieldsHigh correlation
Min is highly overall correlated with MSTV and 4 other fieldsHigh correlation
Max is highly overall correlated with Width and 2 other fieldsHigh correlation
Nmax is highly overall correlated with MSTV and 4 other fieldsHigh correlation
Mode is highly overall correlated with LBE and 5 other fieldsHigh correlation
Mean is highly overall correlated with LBE and 4 other fieldsHigh correlation
Median is highly overall correlated with LBE and 4 other fieldsHigh correlation
Variance is highly overall correlated with MSTV and 7 other fieldsHigh correlation
CLASS is highly overall correlated with A and 10 other fieldsHigh correlation
DS is highly overall correlated with ModeHigh correlation
DP is highly overall correlated with LDHigh correlation
A is highly overall correlated with CLASSHigh correlation
B is highly overall correlated with AC and 1 other fieldsHigh correlation
C is highly overall correlated with CLASSHigh correlation
D is highly overall correlated with CLASSHigh correlation
E is highly overall correlated with CLASSHigh correlation
AD is highly overall correlated with DL and 1 other fieldsHigh correlation
DE is highly overall correlated with DL and 1 other fieldsHigh correlation
LD is highly overall correlated with Mode and 6 other fieldsHigh correlation
FS is highly overall correlated with ASTV and 3 other fieldsHigh correlation
SUSP is highly overall correlated with ALTV and 2 other fieldsHigh correlation
NSP is highly overall correlated with CLASS and 3 other fieldsHigh correlation
DS is highly imbalanced (96.8%)Imbalance
DP is highly imbalanced (77.3%)Imbalance
C is highly imbalanced (83.2%)Imbalance
D is highly imbalanced (76.6%)Imbalance
E is highly imbalanced (78.7%)Imbalance
LD is highly imbalanced (71.2%)Imbalance
FS is highly imbalanced (79.3%)Imbalance
SUSP is highly imbalanced (55.5%)Imbalance
b has 314 (14.8%) zerosZeros
AC has 891 (41.9%) zerosZeros
FM has 1311 (61.7%) zerosZeros
UC has 332 (15.6%) zerosZeros
ALTV has 1240 (58.3%) zerosZeros
MLTV has 137 (6.4%) zerosZeros
DL has 1231 (57.9%) zerosZeros
Nmax has 107 (5.0%) zerosZeros
Nzeros has 1624 (76.4%) zerosZeros
Variance has 187 (8.8%) zerosZeros

Reproduction

Analysis started2023-07-21 12:43:35.148201
Analysis finished2023-07-21 12:45:39.748710
Duration2 minutes and 4.6 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

b
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct979
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean878.43979
Minimum0
Maximum3296
Zeros314
Zeros (%)14.8%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-07-21T09:45:39.988508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q155
median538
Q31521
95-th percentile2654
Maximum3296
Range3296
Interquartile range (IQR)1466

Descriptive statistics

Standard deviation894.08475
Coefficient of variation (CV)1.0178099
Kurtosis-0.54023037
Mean878.43979
Median Absolute Deviation (MAD)536
Skewness0.82989757
Sum1867563
Variance799387.54
MonotonicityNot monotonic
2023-07-21T09:45:40.238473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 314
 
14.8%
8 18
 
0.8%
12 13
 
0.6%
10 13
 
0.6%
30 13
 
0.6%
16 12
 
0.6%
17 12
 
0.6%
21 12
 
0.6%
25 11
 
0.5%
14 9
 
0.4%
Other values (969) 1699
79.9%
ValueCountFrequency (%)
0 314
14.8%
1 3
 
0.1%
2 3
 
0.1%
3 6
 
0.3%
4 2
 
0.1%
5 6
 
0.3%
6 1
 
< 0.1%
7 7
 
0.3%
8 18
 
0.8%
10 13
 
0.6%
ValueCountFrequency (%)
3296 1
< 0.1%
3274 1
< 0.1%
3199 1
< 0.1%
3194 1
< 0.1%
3188 1
< 0.1%
3179 1
< 0.1%
3168 1
< 0.1%
3147 1
< 0.1%
3125 1
< 0.1%
3093 1
< 0.1%

e
Real number (ℝ)

HIGH CORRELATION 

Distinct1064
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1702.8772
Minimum287
Maximum3599
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-07-21T09:45:40.494510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum287
5-th percentile592
Q11009
median1241
Q32434.75
95-th percentile3537.5
Maximum3599
Range3312
Interquartile range (IQR)1425.75

Descriptive statistics

Standard deviation930.91914
Coefficient of variation (CV)0.54667425
Kurtosis-0.82021512
Mean1702.8772
Median Absolute Deviation (MAD)528
Skewness0.66300193
Sum3620317
Variance866610.45
MonotonicityNot monotonic
2023-07-21T09:45:40.738760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1199 155
 
7.3%
3599 38
 
1.8%
3540 23
 
1.1%
1192 21
 
1.0%
1185 15
 
0.7%
1182 15
 
0.7%
1194 13
 
0.6%
1189 11
 
0.5%
1174 11
 
0.5%
1014 10
 
0.5%
Other values (1054) 1814
85.3%
ValueCountFrequency (%)
287 1
< 0.1%
301 1
< 0.1%
307 1
< 0.1%
321 1
< 0.1%
346 1
< 0.1%
357 2
0.1%
361 1
< 0.1%
363 1
< 0.1%
364 1
< 0.1%
370 1
< 0.1%
ValueCountFrequency (%)
3599 38
1.8%
3597 5
 
0.2%
3595 7
 
0.3%
3593 7
 
0.3%
3591 3
 
0.1%
3588 3
 
0.1%
3586 1
 
< 0.1%
3584 1
 
< 0.1%
3582 1
 
< 0.1%
3580 1
 
< 0.1%

LBE
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133.30386
Minimum106
Maximum160
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-07-21T09:45:40.989585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum106
5-th percentile119
Q1126
median133
Q3140
95-th percentile149
Maximum160
Range54
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.8408443
Coefficient of variation (CV)0.073822652
Kurtosis-0.29294291
Mean133.30386
Median Absolute Deviation (MAD)7
Skewness0.020312189
Sum283404
Variance96.842216
MonotonicityNot monotonic
2023-07-21T09:45:41.244791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
133 136
 
6.4%
130 111
 
5.2%
122 109
 
5.1%
138 103
 
4.8%
125 91
 
4.3%
128 85
 
4.0%
120 78
 
3.7%
142 77
 
3.6%
144 77
 
3.6%
132 76
 
3.6%
Other values (38) 1183
55.6%
ValueCountFrequency (%)
106 7
 
0.3%
110 21
 
1.0%
112 16
 
0.8%
114 11
 
0.5%
115 28
 
1.3%
116 5
 
0.2%
117 2
 
0.1%
118 9
 
0.4%
119 17
 
0.8%
120 78
3.7%
ValueCountFrequency (%)
160 1
 
< 0.1%
159 12
0.6%
158 10
 
0.5%
157 4
 
0.2%
156 4
 
0.2%
154 8
 
0.4%
152 17
0.8%
151 14
0.7%
150 26
1.2%
149 18
0.8%

LB
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133.30386
Minimum106
Maximum160
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-07-21T09:45:41.493141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum106
5-th percentile119
Q1126
median133
Q3140
95-th percentile149
Maximum160
Range54
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.8408443
Coefficient of variation (CV)0.073822652
Kurtosis-0.29294291
Mean133.30386
Median Absolute Deviation (MAD)7
Skewness0.020312189
Sum283404
Variance96.842216
MonotonicityNot monotonic
2023-07-21T09:45:41.750666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
133 136
 
6.4%
130 111
 
5.2%
122 109
 
5.1%
138 103
 
4.8%
125 91
 
4.3%
128 85
 
4.0%
120 78
 
3.7%
142 77
 
3.6%
144 77
 
3.6%
132 76
 
3.6%
Other values (38) 1183
55.6%
ValueCountFrequency (%)
106 7
 
0.3%
110 21
 
1.0%
112 16
 
0.8%
114 11
 
0.5%
115 28
 
1.3%
116 5
 
0.2%
117 2
 
0.1%
118 9
 
0.4%
119 17
 
0.8%
120 78
3.7%
ValueCountFrequency (%)
160 1
 
< 0.1%
159 12
0.6%
158 10
 
0.5%
157 4
 
0.2%
156 4
 
0.2%
154 8
 
0.4%
152 17
0.8%
151 14
0.7%
150 26
1.2%
149 18
0.8%

AC
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7224835
Minimum0
Maximum26
Zeros891
Zeros (%)41.9%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-07-21T09:45:41.966308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile10
Maximum26
Range26
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.5608502
Coefficient of variation (CV)1.3079419
Kurtosis3.1224932
Mean2.7224835
Median Absolute Deviation (MAD)1
Skewness1.6588299
Sum5788
Variance12.679654
MonotonicityNot monotonic
2023-07-21T09:45:42.376137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 891
41.9%
1 242
 
11.4%
2 164
 
7.7%
3 162
 
7.6%
4 148
 
7.0%
5 110
 
5.2%
6 104
 
4.9%
7 76
 
3.6%
8 56
 
2.6%
9 50
 
2.4%
Other values (12) 123
 
5.8%
ValueCountFrequency (%)
0 891
41.9%
1 242
 
11.4%
2 164
 
7.7%
3 162
 
7.6%
4 148
 
7.0%
5 110
 
5.2%
6 104
 
4.9%
7 76
 
3.6%
8 56
 
2.6%
9 50
 
2.4%
ValueCountFrequency (%)
26 1
 
< 0.1%
21 1
 
< 0.1%
19 2
 
0.1%
18 2
 
0.1%
17 7
0.3%
16 4
 
0.2%
15 5
 
0.2%
14 13
0.6%
13 15
0.7%
12 17
0.8%

FM
Real number (ℝ)

ZEROS 

Distinct96
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2412982
Minimum0
Maximum564
Zeros1311
Zeros (%)61.7%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-07-21T09:45:42.606972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile22
Maximum564
Range564
Interquartile range (IQR)2

Descriptive statistics

Standard deviation37.125309
Coefficient of variation (CV)5.1268858
Kurtosis104.63437
Mean7.2412982
Median Absolute Deviation (MAD)0
Skewness9.4274963
Sum15395
Variance1378.2886
MonotonicityNot monotonic
2023-07-21T09:45:42.841324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1311
61.7%
1 208
 
9.8%
2 119
 
5.6%
3 85
 
4.0%
4 65
 
3.1%
6 35
 
1.6%
7 28
 
1.3%
5 28
 
1.3%
8 25
 
1.2%
10 24
 
1.1%
Other values (86) 198
 
9.3%
ValueCountFrequency (%)
0 1311
61.7%
1 208
 
9.8%
2 119
 
5.6%
3 85
 
4.0%
4 65
 
3.1%
5 28
 
1.3%
6 35
 
1.6%
7 28
 
1.3%
8 25
 
1.2%
9 13
 
0.6%
ValueCountFrequency (%)
564 1
< 0.1%
557 1
< 0.1%
489 2
0.1%
443 1
< 0.1%
325 2
0.1%
324 1
< 0.1%
317 1
< 0.1%
314 1
< 0.1%
304 1
< 0.1%
290 1
< 0.1%

UC
Real number (ℝ)

ZEROS 

Distinct19
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6599247
Minimum0
Maximum23
Zeros332
Zeros (%)15.6%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-07-21T09:45:43.052731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile9
Maximum23
Range23
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8470935
Coefficient of variation (CV)0.7779104
Kurtosis1.289254
Mean3.6599247
Median Absolute Deviation (MAD)2
Skewness0.8353463
Sum7781
Variance8.1059415
MonotonicityNot monotonic
2023-07-21T09:45:43.245835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 332
15.6%
3 294
13.8%
4 272
12.8%
1 238
11.2%
2 236
11.1%
5 235
11.1%
6 199
9.4%
7 114
 
5.4%
8 82
 
3.9%
9 57
 
2.7%
Other values (9) 67
 
3.2%
ValueCountFrequency (%)
0 332
15.6%
1 238
11.2%
2 236
11.1%
3 294
13.8%
4 272
12.8%
5 235
11.1%
6 199
9.4%
7 114
 
5.4%
8 82
 
3.9%
9 57
 
2.7%
ValueCountFrequency (%)
23 1
 
< 0.1%
17 1
 
< 0.1%
16 1
 
< 0.1%
15 1
 
< 0.1%
14 3
 
0.1%
13 6
 
0.3%
12 9
 
0.4%
11 16
 
0.8%
10 29
1.4%
9 57
2.7%

ASTV
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.990122
Minimum12
Maximum87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-07-21T09:45:43.474905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile21
Q132
median49
Q361
95-th percentile75
Maximum87
Range75
Interquartile range (IQR)29

Descriptive statistics

Standard deviation17.192814
Coefficient of variation (CV)0.36588144
Kurtosis-1.0510296
Mean46.990122
Median Absolute Deviation (MAD)14
Skewness-0.011828576
Sum99901
Variance295.59284
MonotonicityNot monotonic
2023-07-21T09:45:43.731936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 62
 
2.9%
58 61
 
2.9%
65 60
 
2.8%
63 58
 
2.7%
64 58
 
2.7%
61 57
 
2.7%
51 54
 
2.5%
62 51
 
2.4%
22 48
 
2.3%
25 46
 
2.2%
Other values (65) 1571
73.9%
ValueCountFrequency (%)
12 2
 
0.1%
13 7
 
0.3%
14 4
 
0.2%
15 4
 
0.2%
16 12
 
0.6%
17 13
 
0.6%
18 10
 
0.5%
19 19
0.9%
20 27
1.3%
21 33
1.6%
ValueCountFrequency (%)
87 1
 
< 0.1%
86 4
 
0.2%
84 6
 
0.3%
83 4
 
0.2%
82 2
 
0.1%
81 7
 
0.3%
80 7
 
0.3%
79 15
0.7%
78 19
0.9%
77 16
0.8%

MSTV
Real number (ℝ)

HIGH CORRELATION 

Distinct57
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3327846
Minimum0.2
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-07-21T09:45:44.005467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.3
Q10.7
median1.2
Q31.7
95-th percentile3
Maximum7
Range6.8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.88324133
Coefficient of variation (CV)0.66270375
Kurtosis4.7007563
Mean1.3327846
Median Absolute Deviation (MAD)0.5
Skewness1.6573392
Sum2833.5
Variance0.78011525
MonotonicityNot monotonic
2023-07-21T09:45:44.256873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8 125
 
5.9%
1.3 121
 
5.7%
0.5 121
 
5.7%
0.4 120
 
5.6%
0.7 117
 
5.5%
0.9 114
 
5.4%
0.6 113
 
5.3%
1.2 107
 
5.0%
1.5 100
 
4.7%
1 99
 
4.7%
Other values (47) 989
46.5%
ValueCountFrequency (%)
0.2 47
 
2.2%
0.3 84
4.0%
0.4 120
5.6%
0.5 121
5.7%
0.6 113
5.3%
0.7 117
5.5%
0.8 125
5.9%
0.9 114
5.4%
1 99
4.7%
1.1 97
4.6%
ValueCountFrequency (%)
7 1
< 0.1%
6.9 1
< 0.1%
6.3 2
0.1%
6 1
< 0.1%
5.9 1
< 0.1%
5.7 1
< 0.1%
5.4 2
0.1%
5.3 1
< 0.1%
5.2 1
< 0.1%
5 2
0.1%

ALTV
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct87
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8466604
Minimum0
Maximum91
Zeros1240
Zeros (%)58.3%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-07-21T09:45:44.521523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q311
95-th percentile56
Maximum91
Range91
Interquartile range (IQR)11

Descriptive statistics

Standard deviation18.39688
Coefficient of variation (CV)1.868337
Kurtosis4.2529979
Mean9.8466604
Median Absolute Deviation (MAD)0
Skewness2.1950753
Sum20934
Variance338.44518
MonotonicityNot monotonic
2023-07-21T09:45:44.769141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1240
58.3%
1 52
 
2.4%
2 45
 
2.1%
5 43
 
2.0%
4 40
 
1.9%
3 36
 
1.7%
8 34
 
1.6%
6 31
 
1.5%
12 29
 
1.4%
10 23
 
1.1%
Other values (77) 553
26.0%
ValueCountFrequency (%)
0 1240
58.3%
1 52
 
2.4%
2 45
 
2.1%
3 36
 
1.7%
4 40
 
1.9%
5 43
 
2.0%
6 31
 
1.5%
7 23
 
1.1%
8 34
 
1.6%
9 22
 
1.0%
ValueCountFrequency (%)
91 4
0.2%
90 2
 
0.1%
88 1
 
< 0.1%
86 1
 
< 0.1%
85 1
 
< 0.1%
84 6
0.3%
82 1
 
< 0.1%
81 2
 
0.1%
79 1
 
< 0.1%
78 3
0.1%

MLTV
Real number (ℝ)

ZEROS 

Distinct249
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1876294
Minimum0
Maximum50.7
Zeros137
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-07-21T09:45:45.016397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.6
median7.4
Q310.8
95-th percentile18.475
Maximum50.7
Range50.7
Interquartile range (IQR)6.2

Descriptive statistics

Standard deviation5.6282466
Coefficient of variation (CV)0.68740857
Kurtosis4.1312538
Mean8.1876294
Median Absolute Deviation (MAD)3.1
Skewness1.3319979
Sum17406.9
Variance31.67716
MonotonicityNot monotonic
2023-07-21T09:45:45.274660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 137
 
6.4%
7.1 29
 
1.4%
6.7 29
 
1.4%
6.5 25
 
1.2%
5.2 25
 
1.2%
9.5 24
 
1.1%
6.8 23
 
1.1%
5.6 23
 
1.1%
7.2 23
 
1.1%
8.5 23
 
1.1%
Other values (239) 1765
83.0%
ValueCountFrequency (%)
0 137
6.4%
0.1 4
 
0.2%
0.2 4
 
0.2%
0.3 9
 
0.4%
0.4 6
 
0.3%
0.5 11
 
0.5%
0.6 3
 
0.1%
0.7 4
 
0.2%
0.8 1
 
< 0.1%
0.9 5
 
0.2%
ValueCountFrequency (%)
50.7 1
< 0.1%
41.8 1
< 0.1%
40.8 1
< 0.1%
36.9 1
< 0.1%
35.7 1
< 0.1%
34.7 1
< 0.1%
33.5 1
< 0.1%
29.6 1
< 0.1%
29.5 1
< 0.1%
29.3 1
< 0.1%

DL
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5700847
Minimum0
Maximum16
Zeros1231
Zeros (%)57.9%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-07-21T09:45:45.485579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile7
Maximum16
Range16
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4992288
Coefficient of variation (CV)1.5917796
Kurtosis3.1468464
Mean1.5700847
Median Absolute Deviation (MAD)0
Skewness1.8191195
Sum3338
Variance6.2461447
MonotonicityNot monotonic
2023-07-21T09:45:45.680720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 1231
57.9%
1 232
 
10.9%
2 127
 
6.0%
4 123
 
5.8%
3 112
 
5.3%
5 108
 
5.1%
6 67
 
3.2%
7 45
 
2.1%
8 28
 
1.3%
9 25
 
1.2%
Other values (5) 28
 
1.3%
ValueCountFrequency (%)
0 1231
57.9%
1 232
 
10.9%
2 127
 
6.0%
3 112
 
5.3%
4 123
 
5.8%
5 108
 
5.1%
6 67
 
3.2%
7 45
 
2.1%
8 28
 
1.3%
9 25
 
1.2%
ValueCountFrequency (%)
16 1
 
< 0.1%
14 2
 
0.1%
12 6
 
0.3%
11 12
 
0.6%
10 7
 
0.3%
9 25
 
1.2%
8 28
 
1.3%
7 45
2.1%
6 67
3.2%
5 108
5.1%

DS
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
0.0
2119 
1.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6378
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2119
99.7%
1.0 7
 
0.3%

Length

2023-07-21T09:45:45.897319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-21T09:45:46.164439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2119
99.7%
1.0 7
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 4245
66.6%
. 2126
33.3%
1 7
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4252
66.7%
Other Punctuation 2126
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4245
99.8%
1 7
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 2126
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6378
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4245
66.6%
. 2126
33.3%
1 7
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6378
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4245
66.6%
. 2126
33.3%
1 7
 
0.1%

DP
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
0.0
1948 
1.0
 
109
2.0
 
49
3.0
 
19
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6378
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1948
91.6%
1.0 109
 
5.1%
2.0 49
 
2.3%
3.0 19
 
0.9%
4.0 1
 
< 0.1%

Length

2023-07-21T09:45:46.336267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-21T09:45:46.607519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1948
91.6%
1.0 109
 
5.1%
2.0 49
 
2.3%
3.0 19
 
0.9%
4.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 4074
63.9%
. 2126
33.3%
1 109
 
1.7%
2 49
 
0.8%
3 19
 
0.3%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4252
66.7%
Other Punctuation 2126
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4074
95.8%
1 109
 
2.6%
2 49
 
1.2%
3 19
 
0.4%
4 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 2126
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6378
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4074
63.9%
. 2126
33.3%
1 109
 
1.7%
2 49
 
0.8%
3 19
 
0.3%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6378
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4074
63.9%
. 2126
33.3%
1 109
 
1.7%
2 49
 
0.8%
3 19
 
0.3%
4 1
 
< 0.1%

DR
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
0.0
2126 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6378
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2126
100.0%

Length

2023-07-21T09:45:46.834581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-21T09:45:47.081484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2126
100.0%

Most occurring characters

ValueCountFrequency (%)
0 4252
66.7%
. 2126
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4252
66.7%
Other Punctuation 2126
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4252
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2126
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6378
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4252
66.7%
. 2126
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6378
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4252
66.7%
. 2126
33.3%

Width
Real number (ℝ)

HIGH CORRELATION 

Distinct154
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.445908
Minimum3
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-07-21T09:45:47.318862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile16
Q137
median67.5
Q3100
95-th percentile138
Maximum180
Range177
Interquartile range (IQR)63

Descriptive statistics

Standard deviation38.955693
Coefficient of variation (CV)0.55298731
Kurtosis-0.90228678
Mean70.445908
Median Absolute Deviation (MAD)31.5
Skewness0.31423475
Sum149768
Variance1517.546
MonotonicityNot monotonic
2023-07-21T09:45:47.605870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 42
 
2.0%
102 35
 
1.6%
27 30
 
1.4%
31 29
 
1.4%
90 28
 
1.3%
98 28
 
1.3%
96 27
 
1.3%
83 27
 
1.3%
22 27
 
1.3%
42 26
 
1.2%
Other values (144) 1827
85.9%
ValueCountFrequency (%)
3 2
 
0.1%
5 2
 
0.1%
6 1
 
< 0.1%
7 3
 
0.1%
8 10
0.5%
9 6
 
0.3%
10 9
0.4%
11 10
0.5%
12 20
0.9%
13 13
0.6%
ValueCountFrequency (%)
180 1
 
< 0.1%
176 6
0.3%
163 2
 
0.1%
162 1
 
< 0.1%
161 5
0.2%
158 2
 
0.1%
153 3
 
0.1%
150 10
0.5%
149 11
0.5%
148 8
0.4%

Min
Real number (ℝ)

HIGH CORRELATION 

Distinct109
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.579492
Minimum50
Maximum159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-07-21T09:45:47.894572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile51
Q167
median93
Q3120
95-th percentile139
Maximum159
Range109
Interquartile range (IQR)53

Descriptive statistics

Standard deviation29.560212
Coefficient of variation (CV)0.31588344
Kurtosis-1.2904222
Mean93.579492
Median Absolute Deviation (MAD)27
Skewness0.11578402
Sum198950
Variance873.80615
MonotonicityNot monotonic
2023-07-21T09:45:48.396980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 77
 
3.6%
52 50
 
2.4%
71 49
 
2.3%
120 48
 
2.3%
60 45
 
2.1%
68 43
 
2.0%
67 41
 
1.9%
103 40
 
1.9%
51 36
 
1.7%
62 35
 
1.6%
Other values (99) 1662
78.2%
ValueCountFrequency (%)
50 77
3.6%
51 36
1.7%
52 50
2.4%
53 32
1.5%
54 27
 
1.3%
55 20
 
0.9%
56 19
 
0.9%
57 22
 
1.0%
58 22
 
1.0%
59 17
 
0.8%
ValueCountFrequency (%)
159 1
 
< 0.1%
158 1
 
< 0.1%
156 1
 
< 0.1%
155 2
 
0.1%
154 3
 
0.1%
153 8
0.4%
152 4
0.2%
151 4
0.2%
150 3
 
0.1%
149 2
 
0.1%

Max
Real number (ℝ)

HIGH CORRELATION 

Distinct86
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.0254
Minimum122
Maximum238
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-07-21T09:45:48.654159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum122
5-th percentile138
Q1152
median162
Q3174
95-th percentile198
Maximum238
Range116
Interquartile range (IQR)22

Descriptive statistics

Standard deviation17.944183
Coefficient of variation (CV)0.10939881
Kurtosis0.63276948
Mean164.0254
Median Absolute Deviation (MAD)11
Skewness0.57786245
Sum348718
Variance321.99371
MonotonicityNot monotonic
2023-07-21T09:45:48.930267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
157 71
 
3.3%
171 66
 
3.1%
158 62
 
2.9%
156 60
 
2.8%
159 58
 
2.7%
152 54
 
2.5%
154 52
 
2.4%
178 52
 
2.4%
172 48
 
2.3%
153 48
 
2.3%
Other values (76) 1555
73.1%
ValueCountFrequency (%)
122 2
 
0.1%
123 2
 
0.1%
125 3
 
0.1%
126 5
0.2%
127 2
 
0.1%
128 4
 
0.2%
129 10
0.5%
130 8
0.4%
131 7
0.3%
132 4
 
0.2%
ValueCountFrequency (%)
238 6
 
0.3%
230 3
 
0.1%
228 5
 
0.2%
213 1
 
< 0.1%
211 5
 
0.2%
210 4
 
0.2%
205 1
 
< 0.1%
204 3
 
0.1%
200 31
1.5%
199 20
0.9%

Nmax
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0682032
Minimum0
Maximum18
Zeros107
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-07-21T09:45:49.225155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q12
median3
Q36
95-th percentile10
Maximum18
Range18
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9493856
Coefficient of variation (CV)0.72498483
Kurtosis0.50421053
Mean4.0682032
Median Absolute Deviation (MAD)2
Skewness0.89288591
Sum8649
Variance8.6988755
MonotonicityNot monotonic
2023-07-21T09:45:49.442080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 357
16.8%
2 331
15.6%
3 269
12.7%
4 258
12.1%
5 210
9.9%
6 158
7.4%
7 145
6.8%
0 107
 
5.0%
8 106
 
5.0%
9 67
 
3.2%
Other values (8) 118
 
5.6%
ValueCountFrequency (%)
0 107
 
5.0%
1 357
16.8%
2 331
15.6%
3 269
12.7%
4 258
12.1%
5 210
9.9%
6 158
7.4%
7 145
6.8%
8 106
 
5.0%
9 67
 
3.2%
ValueCountFrequency (%)
18 1
 
< 0.1%
16 2
 
0.1%
15 1
 
< 0.1%
14 5
 
0.2%
13 10
 
0.5%
12 22
 
1.0%
11 28
 
1.3%
10 49
2.3%
9 67
3.2%
8 106
5.0%

Nzeros
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.32361242
Minimum0
Maximum10
Zeros1624
Zeros (%)76.4%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-07-21T09:45:49.662269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.70605937
Coefficient of variation (CV)2.1818056
Kurtosis30.365084
Mean0.32361242
Median Absolute Deviation (MAD)0
Skewness3.9202874
Sum688
Variance0.49851984
MonotonicityNot monotonic
2023-07-21T09:45:49.907751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 1624
76.4%
1 366
 
17.2%
2 108
 
5.1%
3 21
 
1.0%
4 2
 
0.1%
5 2
 
0.1%
10 1
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 1624
76.4%
1 366
 
17.2%
2 108
 
5.1%
3 21
 
1.0%
4 2
 
0.1%
5 2
 
0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
5 2
 
0.1%
4 2
 
0.1%
3 21
 
1.0%
2 108
 
5.1%
1 366
 
17.2%
0 1624
76.4%

Mode
Real number (ℝ)

HIGH CORRELATION 

Distinct88
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137.45202
Minimum60
Maximum187
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-07-21T09:45:50.123523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile111.25
Q1129
median139
Q3148
95-th percentile160
Maximum187
Range127
Interquartile range (IQR)19

Descriptive statistics

Standard deviation16.381289
Coefficient of variation (CV)0.11917823
Kurtosis3.0095305
Mean137.45202
Median Absolute Deviation (MAD)10
Skewness-0.99517784
Sum292223
Variance268.34664
MonotonicityNot monotonic
2023-07-21T09:45:50.438162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
133 140
 
6.6%
136 89
 
4.2%
150 89
 
4.2%
142 87
 
4.1%
148 79
 
3.7%
144 78
 
3.7%
129 76
 
3.6%
143 71
 
3.3%
125 67
 
3.2%
126 66
 
3.1%
Other values (78) 1284
60.4%
ValueCountFrequency (%)
60 6
0.3%
67 5
0.2%
69 1
 
< 0.1%
71 1
 
< 0.1%
75 6
0.3%
76 1
 
< 0.1%
77 1
 
< 0.1%
86 11
0.5%
88 6
0.3%
89 3
 
0.1%
ValueCountFrequency (%)
187 1
 
< 0.1%
186 6
0.3%
180 4
 
0.2%
179 1
 
< 0.1%
176 6
0.3%
170 4
 
0.2%
169 3
 
0.1%
167 8
0.4%
165 10
0.5%
164 1
 
< 0.1%

Mean
Real number (ℝ)

HIGH CORRELATION 

Distinct103
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean134.61054
Minimum73
Maximum182
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-07-21T09:45:50.858314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum73
5-th percentile108
Q1125
median136
Q3145
95-th percentile157
Maximum182
Range109
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.593596
Coefficient of variation (CV)0.11584232
Kurtosis0.93342749
Mean134.61054
Median Absolute Deviation (MAD)10
Skewness-0.65101924
Sum286182
Variance243.16025
MonotonicityNot monotonic
2023-07-21T09:45:51.169683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
143 65
 
3.1%
144 64
 
3.0%
135 63
 
3.0%
141 61
 
2.9%
140 60
 
2.8%
132 59
 
2.8%
133 58
 
2.7%
145 58
 
2.7%
136 57
 
2.7%
147 57
 
2.7%
Other values (93) 1524
71.7%
ValueCountFrequency (%)
73 1
 
< 0.1%
75 1
 
< 0.1%
76 1
 
< 0.1%
78 1
 
< 0.1%
79 1
 
< 0.1%
80 2
0.1%
81 1
 
< 0.1%
82 2
0.1%
83 4
0.2%
84 3
0.1%
ValueCountFrequency (%)
182 1
< 0.1%
180 1
< 0.1%
178 1
< 0.1%
175 1
< 0.1%
173 2
0.1%
172 1
< 0.1%
171 2
0.1%
170 1
< 0.1%
169 1
< 0.1%
168 1
< 0.1%

Median
Real number (ℝ)

HIGH CORRELATION 

Distinct95
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138.09031
Minimum77
Maximum186
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-07-21T09:45:51.521339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum77
5-th percentile113
Q1129
median139
Q3148
95-th percentile159
Maximum186
Range109
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.466589
Coefficient of variation (CV)0.1047618
Kurtosis0.66725933
Mean138.09031
Median Absolute Deviation (MAD)10
Skewness-0.4784142
Sum293580
Variance209.28219
MonotonicityNot monotonic
2023-07-21T09:45:51.839041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
146 69
 
3.2%
137 68
 
3.2%
142 68
 
3.2%
145 67
 
3.2%
147 65
 
3.1%
151 63
 
3.0%
141 63
 
3.0%
134 62
 
2.9%
149 60
 
2.8%
143 56
 
2.6%
Other values (85) 1485
69.8%
ValueCountFrequency (%)
77 1
< 0.1%
78 1
< 0.1%
79 2
0.1%
82 1
< 0.1%
86 1
< 0.1%
87 1
< 0.1%
90 1
< 0.1%
91 1
< 0.1%
92 2
0.1%
93 1
< 0.1%
ValueCountFrequency (%)
186 1
< 0.1%
183 1
< 0.1%
180 1
< 0.1%
178 1
< 0.1%
177 1
< 0.1%
176 2
0.1%
174 2
0.1%
172 1
< 0.1%
171 1
< 0.1%
170 2
0.1%

Variance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct133
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.80809
Minimum0
Maximum269
Zeros187
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-07-21T09:45:52.128927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median7
Q324
95-th percentile76
Maximum269
Range269
Interquartile range (IQR)22

Descriptive statistics

Standard deviation28.977636
Coefficient of variation (CV)1.5407006
Kurtosis15.131589
Mean18.80809
Median Absolute Deviation (MAD)6
Skewness3.2199738
Sum39986
Variance839.70339
MonotonicityNot monotonic
2023-07-21T09:45:52.389053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 248
 
11.7%
0 187
 
8.8%
2 166
 
7.8%
3 161
 
7.6%
4 108
 
5.1%
5 85
 
4.0%
8 74
 
3.5%
6 65
 
3.1%
7 53
 
2.5%
9 49
 
2.3%
Other values (123) 930
43.7%
ValueCountFrequency (%)
0 187
8.8%
1 248
11.7%
2 166
7.8%
3 161
7.6%
4 108
5.1%
5 85
 
4.0%
6 65
 
3.1%
7 53
 
2.5%
8 74
 
3.5%
9 49
 
2.3%
ValueCountFrequency (%)
269 1
< 0.1%
254 1
< 0.1%
250 1
< 0.1%
243 1
< 0.1%
241 1
< 0.1%
215 1
< 0.1%
195 1
< 0.1%
190 1
< 0.1%
182 1
< 0.1%
177 1
< 0.1%

Tendency
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
0.0
1115 
1.0
846 
-1.0
165 

Length

Max length4
Median length3
Mean length3.0776105
Min length3

Characters and Unicode

Total characters6543
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 1115
52.4%
1.0 846
39.8%
-1.0 165
 
7.8%

Length

2023-07-21T09:45:52.612742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-21T09:45:52.835573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1115
52.4%
1.0 1011
47.6%

Most occurring characters

ValueCountFrequency (%)
0 3241
49.5%
. 2126
32.5%
1 1011
 
15.5%
- 165
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4252
65.0%
Other Punctuation 2126
32.5%
Dash Punctuation 165
 
2.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3241
76.2%
1 1011
 
23.8%
Other Punctuation
ValueCountFrequency (%)
. 2126
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6543
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3241
49.5%
. 2126
32.5%
1 1011
 
15.5%
- 165
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6543
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3241
49.5%
. 2126
32.5%
1 1011
 
15.5%
- 165
 
2.5%

A
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
0.0
1742 
1.0
384 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6378
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1742
81.9%
1.0 384
 
18.1%

Length

2023-07-21T09:45:53.016989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-21T09:45:53.225832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1742
81.9%
1.0 384
 
18.1%

Most occurring characters

ValueCountFrequency (%)
0 3868
60.6%
. 2126
33.3%
1 384
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4252
66.7%
Other Punctuation 2126
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3868
91.0%
1 384
 
9.0%
Other Punctuation
ValueCountFrequency (%)
. 2126
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6378
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3868
60.6%
. 2126
33.3%
1 384
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6378
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3868
60.6%
. 2126
33.3%
1 384
 
6.0%

B
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
0.0
1547 
1.0
579 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6378
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 1547
72.8%
1.0 579
 
27.2%

Length

2023-07-21T09:45:53.402651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-21T09:45:53.612560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1547
72.8%
1.0 579
 
27.2%

Most occurring characters

ValueCountFrequency (%)
0 3673
57.6%
. 2126
33.3%
1 579
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4252
66.7%
Other Punctuation 2126
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3673
86.4%
1 579
 
13.6%
Other Punctuation
ValueCountFrequency (%)
. 2126
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6378
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3673
57.6%
. 2126
33.3%
1 579
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6378
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3673
57.6%
. 2126
33.3%
1 579
 
9.1%

C
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
0.0
2073 
1.0
 
53

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6378
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2073
97.5%
1.0 53
 
2.5%

Length

2023-07-21T09:45:53.790549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-21T09:45:53.997471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2073
97.5%
1.0 53
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 4199
65.8%
. 2126
33.3%
1 53
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4252
66.7%
Other Punctuation 2126
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4199
98.8%
1 53
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 2126
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6378
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4199
65.8%
. 2126
33.3%
1 53
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6378
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4199
65.8%
. 2126
33.3%
1 53
 
0.8%

D
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
0.0
2045 
1.0
 
81

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6378
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2045
96.2%
1.0 81
 
3.8%

Length

2023-07-21T09:45:54.186687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-21T09:45:54.481911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2045
96.2%
1.0 81
 
3.8%

Most occurring characters

ValueCountFrequency (%)
0 4171
65.4%
. 2126
33.3%
1 81
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4252
66.7%
Other Punctuation 2126
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4171
98.1%
1 81
 
1.9%
Other Punctuation
ValueCountFrequency (%)
. 2126
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6378
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4171
65.4%
. 2126
33.3%
1 81
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6378
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4171
65.4%
. 2126
33.3%
1 81
 
1.3%

E
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
0.0
2054 
1.0
 
72

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6378
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2054
96.6%
1.0 72
 
3.4%

Length

2023-07-21T09:45:54.682671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-21T09:45:54.926375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2054
96.6%
1.0 72
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 4180
65.5%
. 2126
33.3%
1 72
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4252
66.7%
Other Punctuation 2126
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4180
98.3%
1 72
 
1.7%
Other Punctuation
ValueCountFrequency (%)
. 2126
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6378
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4180
65.5%
. 2126
33.3%
1 72
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6378
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4180
65.5%
. 2126
33.3%
1 72
 
1.1%

AD
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
0.0
1794 
1.0
332 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6378
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1794
84.4%
1.0 332
 
15.6%

Length

2023-07-21T09:45:55.101283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-21T09:45:55.328633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1794
84.4%
1.0 332
 
15.6%

Most occurring characters

ValueCountFrequency (%)
0 3920
61.5%
. 2126
33.3%
1 332
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4252
66.7%
Other Punctuation 2126
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3920
92.2%
1 332
 
7.8%
Other Punctuation
ValueCountFrequency (%)
. 2126
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6378
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3920
61.5%
. 2126
33.3%
1 332
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6378
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3920
61.5%
. 2126
33.3%
1 332
 
5.2%

DE
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
0.0
1874 
1.0
252 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6378
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1874
88.1%
1.0 252
 
11.9%

Length

2023-07-21T09:45:55.523007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-21T09:45:55.779113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1874
88.1%
1.0 252
 
11.9%

Most occurring characters

ValueCountFrequency (%)
0 4000
62.7%
. 2126
33.3%
1 252
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4252
66.7%
Other Punctuation 2126
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4000
94.1%
1 252
 
5.9%
Other Punctuation
ValueCountFrequency (%)
. 2126
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6378
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4000
62.7%
. 2126
33.3%
1 252
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6378
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4000
62.7%
. 2126
33.3%
1 252
 
4.0%

LD
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
0.0
2019 
1.0
 
107

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6378
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2019
95.0%
1.0 107
 
5.0%

Length

2023-07-21T09:45:55.998299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-21T09:45:56.279095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2019
95.0%
1.0 107
 
5.0%

Most occurring characters

ValueCountFrequency (%)
0 4145
65.0%
. 2126
33.3%
1 107
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4252
66.7%
Other Punctuation 2126
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4145
97.5%
1 107
 
2.5%
Other Punctuation
ValueCountFrequency (%)
. 2126
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6378
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4145
65.0%
. 2126
33.3%
1 107
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6378
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4145
65.0%
. 2126
33.3%
1 107
 
1.7%

FS
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
0.0
2057 
1.0
 
69

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6378
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2057
96.8%
1.0 69
 
3.2%

Length

2023-07-21T09:45:56.488278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-21T09:45:57.005141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2057
96.8%
1.0 69
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 4183
65.6%
. 2126
33.3%
1 69
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4252
66.7%
Other Punctuation 2126
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4183
98.4%
1 69
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 2126
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6378
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4183
65.6%
. 2126
33.3%
1 69
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6378
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4183
65.6%
. 2126
33.3%
1 69
 
1.1%

SUSP
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
0.0
1929 
1.0
197 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6378
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1929
90.7%
1.0 197
 
9.3%

Length

2023-07-21T09:45:57.252230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-21T09:45:57.630489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1929
90.7%
1.0 197
 
9.3%

Most occurring characters

ValueCountFrequency (%)
0 4055
63.6%
. 2126
33.3%
1 197
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4252
66.7%
Other Punctuation 2126
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4055
95.4%
1 197
 
4.6%
Other Punctuation
ValueCountFrequency (%)
. 2126
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6378
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4055
63.6%
. 2126
33.3%
1 197
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6378
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4055
63.6%
. 2126
33.3%
1 197
 
3.1%

CLASS
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5098777
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-07-21T09:45:57.842607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q37
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.0268829
Coefficient of variation (CV)0.6711674
Kurtosis-1.2290404
Mean4.5098777
Median Absolute Deviation (MAD)2
Skewness0.38116341
Sum9588
Variance9.16202
MonotonicityNot monotonic
2023-07-21T09:45:58.052562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 579
27.2%
1 384
18.1%
6 332
15.6%
7 252
11.9%
10 197
 
9.3%
8 107
 
5.0%
4 81
 
3.8%
5 72
 
3.4%
9 69
 
3.2%
3 53
 
2.5%
ValueCountFrequency (%)
1 384
18.1%
2 579
27.2%
3 53
 
2.5%
4 81
 
3.8%
5 72
 
3.4%
6 332
15.6%
7 252
11.9%
8 107
 
5.0%
9 69
 
3.2%
10 197
 
9.3%
ValueCountFrequency (%)
10 197
 
9.3%
9 69
 
3.2%
8 107
 
5.0%
7 252
11.9%
6 332
15.6%
5 72
 
3.4%
4 81
 
3.8%
3 53
 
2.5%
2 579
27.2%
1 384
18.1%

NSP
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
1.0
1655 
2.0
295 
3.0
176 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6378
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1655
77.8%
2.0 295
 
13.9%
3.0 176
 
8.3%

Length

2023-07-21T09:45:58.248546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-21T09:45:58.473108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1655
77.8%
2.0 295
 
13.9%
3.0 176
 
8.3%

Most occurring characters

ValueCountFrequency (%)
. 2126
33.3%
0 2126
33.3%
1 1655
25.9%
2 295
 
4.6%
3 176
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4252
66.7%
Other Punctuation 2126
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2126
50.0%
1 1655
38.9%
2 295
 
6.9%
3 176
 
4.1%
Other Punctuation
ValueCountFrequency (%)
. 2126
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6378
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 2126
33.3%
0 2126
33.3%
1 1655
25.9%
2 295
 
4.6%
3 176
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6378
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 2126
33.3%
0 2126
33.3%
1 1655
25.9%
2 295
 
4.6%
3 176
 
2.8%

Interactions

2023-07-21T09:45:33.280854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:43:52.564359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:43:57.757349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:03.051514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:07.803721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:12.836097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:17.704656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:22.906864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:27.529130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:32.886482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:37.485205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:41.867158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:46.514935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:51.498691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:56.288937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:00.728093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:05.652103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:10.260032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:14.886995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:19.707167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:24.351237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:28.801957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:33.486166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:43:53.003893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:43:57.967728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:03.274369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:08.008200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:13.037818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:17.931964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:23.127287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:27.742879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:33.094371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:37.683375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:42.070912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:46.802313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:51.709028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:56.491951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:00.935440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:05.864081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:10.468434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:15.092686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:19.908775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:24.554359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:29.006631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:33.689614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:43:53.219858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:43:58.178500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:03.485014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:08.236645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:13.253759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:18.156800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:23.345212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:27.958195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:33.308411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:37.887018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:42.460403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:47.178210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:51.918463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:56.695404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:01.144875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:06.073214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:10.672380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:15.303748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:20.110215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:24.757694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:29.210779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:33.894900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:43:53.427690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:43:58.400478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:03.701795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:08.457788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:13.453887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:18.431635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:23.543962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:28.167980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:33.514579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:38.081512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:42.658583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:47.474257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:52.122925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:56.890328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:01.347002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:06.282514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:10.871984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:15.512819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:20.309623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:24.957904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:29.414331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:34.120356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:43:53.636243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:43:58.629403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:03.896713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:08.684513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:13.672654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:18.718111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:23.737736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:28.376267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:33.720318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:38.281754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:42.862411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:47.687830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:52.334156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:57.085621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:01.551600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:06.488120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:11.069706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:15.711481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:20.502705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:25.156932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:29.614746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:34.331060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:43:53.885631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:43:58.959347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:04.098834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:08.957879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:13.888345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:18.927580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:23.927181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:28.629179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:33.924332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:38.472756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:43.057463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:47.895704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:52.534057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:57.288534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:01.754197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:06.690482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:11.268506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:15.911969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:20.702235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:25.356415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:29.810295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:34.549236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:43:54.117261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-07-21T09:44:16.238011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:21.175813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:26.113862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:31.115987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:36.021054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:40.489033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:45.092235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:50.028815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:54.838896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:59.328535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:04.225590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:08.814604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:13.496931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:17.986705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:22.757716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:27.396165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:31.863178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:37.065986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:43:56.537480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:01.795848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:06.586069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:11.619998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:16.452862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:21.615456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:26.327448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:31.409503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:36.241414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:40.700485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:45.302581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:50.250053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:55.061205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:59.545781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:04.448725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:09.030709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:13.708264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:18.203556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:22.969857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:27.608559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:32.078565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:37.258731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:43:56.740371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:02.017540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:06.795473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:11.823021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:16.652746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:21.812846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:26.556006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:31.632588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:36.453980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:40.896209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:45.499459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:50.457095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:55.268756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:59.740934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:04.648157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:09.237893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:13.904971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:18.422942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:23.170506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:27.810368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:32.274893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:37.463300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:43:56.951248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:02.234431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:07.009934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:12.038320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:16.858220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:22.026129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:26.762132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:31.847994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:36.666341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:41.096004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:45.702076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:50.673404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:55.478029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:59.944287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:04.854300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:09.450068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:14.106509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:18.654246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:23.374677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:28.014325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:32.483297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:37.658124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:43:57.153239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:02.446165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:07.212595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:12.237436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:17.061328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:22.227602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:26.956756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:32.061146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:36.866622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:41.293715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:45.899384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:50.881814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:55.682772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:00.140057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:05.055828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:09.655927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:14.307308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:19.005719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:23.572018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:28.214925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:32.683955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:37.851377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:43:57.357758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:02.645389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:07.411042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:12.437663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:17.265264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:22.444500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:27.150586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:32.479501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:37.084430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:41.486792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:46.095303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:51.091375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:55.886734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:00.340251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:05.256558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:09.861140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:14.501576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:19.243687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:23.771414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:28.412418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:32.887425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:38.043998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:43:57.558542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:02.846890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:07.612413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:12.639459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:17.497778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:22.655489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:27.345373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:32.688226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:37.287713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:41.679751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:46.299651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:51.296052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:44:56.090529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:00.537764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:05.459529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:10.065622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:14.698225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:19.511904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:23.970211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:28.611536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T09:45:33.082765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-21T09:45:58.781938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
beLBELBACFMUCASTVMSTVALTVMLTVDLWidthMinMaxNmaxNzerosModeMeanMedianVarianceCLASSDSDPTendencyABCDEADDELDFSSUSPNSP
b1.0000.847-0.004-0.004-0.013-0.2620.246-0.0990.122-0.124-0.1190.127-0.0720.072-0.068-0.0830.036-0.009-0.071-0.0450.072-0.0610.1200.0860.0640.0320.0000.0320.0760.0170.1330.0820.1440.1340.1640.137
e0.8471.0000.0110.0110.119-0.1950.416-0.0460.155-0.109-0.0940.2090.0150.0090.0060.0140.0330.021-0.055-0.0120.133-0.0610.2070.0890.0530.0930.0850.1170.1040.0450.1750.1240.1280.1720.2170.173
LBE-0.0040.0111.0001.000-0.112-0.024-0.1050.317-0.3670.340-0.055-0.168-0.1550.3580.326-0.121-0.0650.8180.7880.841-0.2390.1140.0860.1220.2750.1000.1270.0960.0920.1840.1930.0990.2260.1750.3130.295
LB-0.0040.0111.0001.000-0.112-0.024-0.1050.317-0.3670.340-0.055-0.168-0.1550.3580.326-0.121-0.0650.8180.7880.841-0.2390.1140.0860.1220.2750.1000.1270.0960.0920.1840.1930.0990.2260.1750.3130.295
AC-0.0130.119-0.112-0.1121.0000.0760.237-0.3160.326-0.473-0.0870.0410.359-0.1730.4720.249-0.0000.1820.1970.2040.396-0.2330.0000.0000.0610.3620.5460.1100.3510.1290.3440.2830.1280.1310.2470.274
FM-0.262-0.195-0.024-0.0240.0761.000-0.2310.2630.082-0.080-0.1170.0580.200-0.1890.1250.203-0.095-0.033-0.081-0.0390.1270.1460.0000.1820.0630.0430.0390.0000.1380.0000.1340.0000.2320.0170.0000.120
UC0.2460.416-0.105-0.1050.237-0.2311.000-0.1160.301-0.249-0.0930.3540.157-0.1040.1510.1350.042-0.051-0.138-0.0860.282-0.0740.0130.2460.0770.0860.1020.1000.0000.0000.2540.0900.1300.1980.2590.196
ASTV-0.099-0.0460.3170.317-0.3160.263-0.1161.000-0.5210.425-0.338-0.124-0.2790.269-0.126-0.166-0.1740.1260.1370.164-0.3450.2440.1140.1350.1570.1830.2570.1420.1860.1820.2670.2580.2710.6480.4670.453
MSTV0.1220.155-0.367-0.3670.3260.0820.301-0.5211.000-0.685-0.0200.6110.714-0.6950.3720.5520.296-0.313-0.460-0.3590.7790.0900.0480.1980.0560.2990.2010.1020.1310.1440.3650.2840.3330.2460.4210.333
ALTV-0.124-0.1090.3400.340-0.473-0.080-0.2490.425-0.6851.000-0.043-0.383-0.5260.465-0.321-0.359-0.1650.2220.2920.245-0.6500.0660.0000.0530.0270.2220.2510.0470.1000.2200.2080.1080.1210.6970.5360.479
MLTV-0.119-0.094-0.055-0.055-0.087-0.117-0.093-0.338-0.020-0.0431.000-0.2530.048-0.081-0.046-0.0020.0720.0020.0770.011-0.093-0.2630.0000.1030.1010.2070.0930.3830.1090.1050.0700.0740.2400.1160.1500.220
DL0.1270.209-0.168-0.1680.0410.0580.354-0.1240.611-0.383-0.2531.0000.586-0.5980.2450.4820.267-0.239-0.459-0.3140.7090.4370.2620.1890.0920.2910.3740.0780.1110.1080.5960.5070.3530.1050.2010.203
Width-0.0720.015-0.155-0.1550.3590.2000.157-0.2790.714-0.5260.0480.5861.000-0.9050.6490.7750.332-0.103-0.234-0.1350.8300.2110.0970.1610.2460.4140.3190.0840.1790.0850.3880.2710.2690.3580.3750.292
Min0.0720.0090.3580.358-0.173-0.189-0.1040.269-0.6950.465-0.081-0.598-0.9051.000-0.292-0.700-0.3270.3460.4710.387-0.751-0.2150.0830.1780.2140.3420.2030.1000.1220.1830.3910.3330.3170.2230.3710.313
Max-0.0680.0060.3260.3260.4720.1250.151-0.1260.372-0.321-0.0460.2450.649-0.2921.0000.5020.1680.4120.3420.4130.5270.1030.0500.1300.1700.3540.2440.0980.2580.0710.2670.0860.1690.2590.1910.143
Nmax-0.0830.014-0.121-0.1210.2490.2030.135-0.1660.552-0.359-0.0020.4820.775-0.7000.5021.0000.293-0.072-0.191-0.1060.6470.1910.0000.1590.1420.2980.1150.0000.1350.0000.2680.2040.1990.2020.2110.124
Nzeros0.0360.033-0.065-0.065-0.000-0.0950.042-0.1740.296-0.1650.0720.2670.332-0.3270.1680.2931.000-0.073-0.116-0.0740.3020.1240.0000.0000.0400.0710.0830.0000.0000.0000.0510.2020.0420.0000.0530.047
Mode-0.0090.0210.8180.8180.182-0.033-0.0510.126-0.3130.2220.002-0.239-0.1030.3460.412-0.072-0.0731.0000.9080.961-0.152-0.0280.5290.3180.3700.1410.1240.0830.4650.2920.0970.1530.7110.0450.1920.433
Mean-0.071-0.0550.7880.7880.197-0.081-0.1380.137-0.4600.2920.077-0.459-0.2340.4710.342-0.191-0.1160.9081.0000.958-0.325-0.1420.4040.3780.3060.1350.2140.1090.3640.2570.2010.3260.8450.0470.2450.496
Median-0.045-0.0120.8410.8410.204-0.039-0.0860.164-0.3590.2450.011-0.314-0.1350.3870.413-0.106-0.0740.9610.9581.000-0.216-0.0750.4680.3440.3550.1410.1410.1040.3990.2500.1370.2060.7360.0540.2130.434
Variance0.0720.133-0.239-0.2390.3960.1270.282-0.3450.779-0.650-0.0930.7090.830-0.7510.5270.6470.302-0.152-0.325-0.2161.0000.2720.2300.3230.1450.2420.2520.0550.1670.0760.3890.2280.5080.0440.1570.267
CLASS-0.061-0.0610.1140.114-0.2330.146-0.0740.2440.0900.066-0.2630.4370.211-0.2150.1030.1910.124-0.028-0.142-0.0750.2721.0000.2030.4150.2540.9980.9980.9980.9980.9980.9980.9980.9980.9980.9980.964
DS0.1200.2070.0860.0860.0000.0000.0130.1140.0480.0000.0000.2620.0970.0830.0500.0000.0000.5290.4040.4680.2300.2031.0000.0440.1350.0000.0140.0000.0000.0000.0000.0000.1920.0000.0000.159
DP0.0860.0890.1220.1220.0000.1820.2460.1350.1980.0530.1030.1890.1610.1780.1300.1590.0000.3180.3780.3440.3230.4150.0441.0000.1680.1350.1800.0210.0420.0360.1610.1660.7900.0000.0860.426
Tendency0.0640.0530.2750.2750.0610.0630.0770.1570.0560.0270.1010.0920.2460.2140.1700.1420.0400.3700.3060.3550.1450.2540.1350.1681.0000.0470.1190.0000.1150.0290.0900.1280.2910.0000.0510.169
A0.0320.0930.1000.1000.3620.0430.0860.1830.2990.2220.2070.2910.4140.3420.3540.2980.0710.1410.1350.1410.2420.9980.0000.1350.0471.0000.2850.0680.0880.0820.1990.1690.1030.0800.1460.243
B0.0000.0850.1270.1270.5460.0390.1020.2570.2010.2510.0930.3740.3190.2030.2440.1150.0830.1240.2140.1410.2520.9980.0140.1800.1190.2851.0000.0920.1170.1100.2610.2220.1370.1070.1930.325
C0.0320.1170.0960.0960.1100.0000.1000.1420.1020.0470.3830.0780.0840.1000.0980.0000.0000.0830.1090.1040.0550.9980.0000.0210.0000.0680.0921.0000.0100.0000.0610.0490.0210.0000.0400.080
D0.0760.1040.0920.0920.3510.1380.0000.1860.1310.1000.1090.1110.1790.1220.2580.1350.0000.4650.3640.3990.1670.9980.0000.0420.1150.0880.1170.0101.0000.0210.0790.0660.0340.0200.0550.102
E0.0170.0450.1840.1840.1290.0000.0000.1820.1440.2200.1050.1080.0850.1830.0710.0000.0000.2920.2570.2500.0760.9980.0000.0360.0290.0820.1100.0000.0211.0000.0740.0610.0300.0160.0510.443
AD0.1330.1750.1930.1930.3440.1340.2540.2670.3650.2080.0700.5960.3880.3910.2670.2680.0510.0970.2010.1370.3890.9980.0000.1610.0900.1990.2610.0610.0790.0741.0000.1540.0940.0720.1340.221
DE0.0820.1240.0990.0990.2830.0000.0900.2580.2840.1080.0740.5070.2710.3330.0860.2040.2020.1530.3260.2060.2280.9980.0000.1660.1280.1690.2220.0490.0660.0610.1541.0000.0780.0590.1130.120
LD0.1440.1280.2260.2260.1280.2320.1300.2710.3330.1210.2400.3530.2690.3170.1690.1990.0420.7110.8450.7360.5080.9980.1920.7900.2910.1030.1370.0210.0340.0300.0940.0781.0000.0290.0660.766
FS0.1340.1720.1750.1750.1310.0170.1980.6480.2460.6970.1160.1050.3580.2230.2590.2020.0000.0450.0470.0540.0440.9980.0000.0000.0000.0800.1070.0000.0200.0160.0720.0590.0291.0000.0490.599
SUSP0.1640.2170.3130.3130.2470.0000.2590.4670.4210.5360.1500.2010.3750.3710.1910.2110.0530.1920.2450.2130.1570.9980.0000.0860.0510.1460.1930.0400.0550.0510.1340.1130.0660.0491.0000.791
NSP0.1370.1730.2950.2950.2740.1200.1960.4530.3330.4790.2200.2030.2920.3130.1430.1240.0470.4330.4960.4340.2670.9640.1590.4260.1690.2430.3250.0800.1020.4430.2210.1200.7660.5990.7911.000

Missing values

2023-07-21T09:45:38.424492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-21T09:45:39.345253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

beLBELBACFMUCASTVMSTVALTVMLTVDLDSDPDRWidthMinMaxNmaxNzerosModeMeanMedianVarianceTendencyABCDEADDELDFSSUSPCLASSNSP
1240.0357.0120.0120.00.00.00.073.00.543.02.40.00.00.00.064.062.0126.02.00.0120.0137.0121.073.01.00.00.00.00.00.00.00.00.01.00.09.02.0
25.0632.0132.0132.04.00.04.017.02.10.010.42.00.00.00.0130.068.0198.06.01.0141.0136.0140.012.00.00.00.00.00.00.01.00.00.00.00.06.01.0
3177.0779.0133.0133.02.00.05.016.02.10.013.42.00.00.00.0130.068.0198.05.01.0141.0135.0138.013.00.00.00.00.00.00.01.00.00.00.00.06.01.0
4411.01192.0134.0134.02.00.06.016.02.40.023.02.00.00.00.0117.053.0170.011.00.0137.0134.0137.013.01.00.00.00.00.00.01.00.00.00.00.06.01.0
5533.01147.0132.0132.04.00.05.016.02.40.019.90.00.00.00.0117.053.0170.09.00.0137.0136.0138.011.01.00.01.00.00.00.00.00.00.00.00.02.01.0
60.0953.0134.0134.01.00.010.026.05.90.00.09.00.02.00.0150.050.0200.05.03.076.0107.0107.0170.00.00.00.00.00.00.00.00.01.00.00.08.03.0
7240.0953.0134.0134.01.00.09.029.06.30.00.06.00.02.00.0150.050.0200.06.03.071.0107.0106.0215.00.00.00.00.00.00.00.00.01.00.00.08.03.0
862.0679.0122.0122.00.00.00.083.00.56.015.60.00.00.00.068.062.0130.00.00.0122.0122.0123.03.01.00.00.00.00.00.00.00.00.01.00.09.03.0
9120.0779.0122.0122.00.00.01.084.00.55.013.60.00.00.00.068.062.0130.00.00.0122.0122.0123.03.01.00.00.00.00.00.00.00.00.01.00.09.03.0
10181.01192.0122.0122.00.00.03.086.00.36.010.60.00.00.00.068.062.0130.01.00.0122.0122.0123.01.01.00.00.00.00.00.00.00.00.01.00.09.03.0
beLBELBACFMUCASTVMSTVALTVMLTVDLDSDPDRWidthMinMaxNmaxNzerosModeMeanMedianVarianceTendencyABCDEADDELDFSSUSPCLASSNSP
2117455.0707.0140.0140.01.00.01.080.00.236.02.20.00.00.00.018.0140.0158.01.00.0147.0148.0149.01.00.00.01.00.00.00.00.00.00.00.00.02.01.0
2118595.01363.0140.0140.00.00.06.079.00.320.08.50.00.00.00.026.0124.0150.01.00.0144.0143.0145.01.01.01.00.00.00.00.00.00.00.00.00.01.01.0
2119595.01677.0140.0140.00.00.07.079.00.526.07.01.00.00.00.021.0129.0150.01.00.0145.0142.0145.02.01.01.00.00.00.00.00.00.00.00.00.01.01.0
2120790.01677.0140.0140.00.00.06.079.00.627.06.41.00.00.00.026.0124.0150.01.00.0144.0141.0145.01.01.01.00.00.00.00.00.00.00.00.00.01.01.0
21211143.01947.0140.0140.00.00.04.077.00.717.06.01.00.00.00.031.0124.0155.02.00.0145.0143.0145.02.00.01.00.00.00.00.00.00.00.00.00.01.01.0
21222059.02867.0140.0140.00.00.06.079.00.225.07.20.00.00.00.040.0137.0177.04.00.0153.0150.0152.02.00.00.00.00.00.01.00.00.00.00.00.05.02.0
21231576.02867.0140.0140.01.00.09.078.00.422.07.10.00.00.00.066.0103.0169.06.00.0152.0148.0151.03.01.00.00.00.00.01.00.00.00.00.00.05.02.0
21241576.02596.0140.0140.01.00.07.079.00.420.06.10.00.00.00.067.0103.0170.05.00.0153.0148.0152.04.01.00.00.00.00.01.00.00.00.00.00.05.02.0
21251576.03049.0140.0140.01.00.09.078.00.427.07.00.00.00.00.066.0103.0169.06.00.0152.0147.0151.04.01.00.00.00.00.01.00.00.00.00.00.05.02.0
21262796.03415.0142.0142.01.01.05.074.00.436.05.00.00.00.00.042.0117.0159.02.01.0145.0143.0145.01.00.01.00.00.00.00.00.00.00.00.00.01.01.0

Duplicate rows

Most frequently occurring

beLBELBACFMUCASTVMSTVALTVMLTVDLDSDPDRWidthMinMaxNmaxNzerosModeMeanMedianVarianceTendencyABCDEADDELDFSSUSPCLASSNSP# duplicates
7569.01320.0122.0122.00.00.00.019.01.90.015.10.00.00.00.039.0103.0142.01.00.0120.0120.0122.03.00.00.00.01.00.00.00.00.00.00.00.03.01.03
00.0809.0144.0144.00.015.00.076.00.461.010.60.00.00.00.081.071.0152.03.00.0145.0144.0146.02.01.00.00.00.00.00.00.00.00.00.01.010.02.02
10.01199.0123.0123.03.04.00.052.00.82.015.40.00.00.00.090.050.0140.07.00.0129.0128.0130.04.01.00.01.00.00.00.00.00.00.00.00.02.01.02
210.0697.0140.0140.05.00.03.034.01.20.010.30.00.00.00.060.0119.0179.02.00.0156.0153.0155.05.00.00.01.00.00.00.00.00.00.00.00.02.01.02
3176.0928.0123.0123.02.03.00.050.00.94.014.80.00.00.00.082.058.0140.07.00.0129.0128.0130.05.01.00.01.00.00.00.00.00.00.00.00.02.01.02
4179.0666.0135.0135.00.00.00.062.00.571.06.90.00.00.00.097.071.0168.03.00.0143.0142.0144.01.01.00.00.00.00.00.00.00.00.01.00.09.03.02
5276.0596.0123.0123.00.00.00.049.00.87.013.80.00.00.00.074.063.0137.02.00.0129.0127.0129.02.01.01.00.00.00.00.00.00.00.00.00.01.01.02
6454.01853.0146.0146.00.00.04.065.00.439.07.00.00.00.00.019.0137.0156.01.00.0150.0149.0151.01.01.00.00.00.00.00.00.00.00.00.01.010.02.02
81544.01968.0148.0148.02.00.01.040.00.90.010.60.00.00.00.035.0136.0171.01.00.0153.0155.0156.04.00.00.01.00.00.00.00.00.00.00.00.02.01.02